A Rolling Optimized Nonlinear Grey Bernoulli Model RONGBM(1,1) and application in predicting total COVID-19 infected cases

TL;DR

This paper introduces a novel rolling optimized nonlinear grey Bernoulli model that enhances forecasting accuracy for small, nonlinear datasets, demonstrated through Vietnam's GDP and global COVID-19 infection predictions.

Contribution

The paper proposes a new RONGBM(1,1) model that combines rolling mechanisms with parameter optimization to improve forecasting accuracy in small nonlinear datasets.

Findings

Significantly improved GDP forecasting accuracy for Vietnam (2013-2018).

Effective prediction of global COVID-19 infected cases.

Demonstrated robustness of the model in real-world applications.

Abstract

The Nonlinear Grey Bernoulli Model NGBM(1, 1) is a recently developed grey model which has various applications in different fields, mainly due to its accuracy in handling small time-series datasets with nonlinear variations. In this paper, to fully improve the accuracy of this model, a novel model is proposed, namely Rolling Optimized Nonlinear Grey Bernoulli Model RONGBM(1, 1). This model combines the rolling mechanism with the simultaneous optimization of all model parameters (exponential, background value and initial condition). The accuracy of this new model has significantly been proven through forecasting Vietnam's GDP from 2013 to 2018, before it is applied to predict the total COVID-19 infected cases globally by day.